GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
Do large language models need a content delivery network?
3 Pith papers cite this work. Polarity classification is still indexing.
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MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
KV-RM regularizes KV-cache movement via block paging and coalesced transfers to improve throughput, tail latency, and memory efficiency in static-graph LLM serving without changing the decoder interface.
citing papers explorer
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Cache Your Prompt When It's Green: Carbon-Aware Caching for Large Language Model Serving
GreenCache dynamically manages LLM KV cache resources to reduce carbon emissions by 15.1% on average (up to 25.3%) while meeting latency constraints for over 90% of requests on real traces.
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MIST: A Co-Design Framework for Heterogeneous, Multi-Stage LLM Inference
MIST is a new simulator for heterogeneous multi-stage LLM inference that combines hardware traces with analytical models to explore configuration trade-offs in hybrid CPU-accelerator systems.
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KV-RM: Regularizing KV-Cache Movement for Static-Graph LLM Serving
KV-RM regularizes KV-cache movement via block paging and coalesced transfers to improve throughput, tail latency, and memory efficiency in static-graph LLM serving without changing the decoder interface.